Journal
MACHINES
Volume 10, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/machines10040240
Keywords
fault diagnosis; fault prediction; PCA; deep learning; nuclear valves
Funding
- State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment [K-A2020.407]
- National Science Foundation of China [52077213, 62073232, 62003332]
- National Natural Science Foundation of Shenzhen [U1813222, U20A20283]
- Natural Science Foundation of Shanxi Province, China [202103021231]
- Guangdong special branch plans young talent with scientific and technological innovation [2019TQ05Z654]
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In this paper, a deep learning fault detection and prediction framework combining PCA and Informer is proposed to solve the problem of online monitoring of nuclear power valves. The effectiveness of the framework for fault diagnosis and prediction of nuclear power valves is demonstrated, enabling online monitoring and maintenance of important equipment without shutting down the nuclear station.
In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with multi-dimensional space to low-dimensional space and extracts the main features. At the same time, the T-square and Q statistic thresholds are also provided to realize abnormal status monitoring. Meanwhile, Informer is a long-term series prediction method. It encrypts and decrypts data through the encoder and decoder to train a prediction model. Through the training of fault data, fault prediction can be realized. Experiments based on the sound waves collected from real valves can be continued, which also illustrates the effectiveness of the PCA-Informer model for fault diagnosis and fault prediction of nuclear power valves. Therefore, the online monitoring and maintenance of nuclear valves and other important equipment, without shutting down the nuclear station, can be achieved.
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